# NeuralEQ: Neural-Network-Based Equalizer for High-Speed Wireline Communication

arXiv: https://arxiv.org/abs/2308.02133 · HTML: https://mlsanity.com/paper/2308.02133/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 4 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [AdaNN: Adaptive Neural Network-based Equalizer via Online Semi-supervised Learning](https://mlsanity.com/paper/1907.10258/) (2019)
- [Performance Comparison of 112 Gb/s DMT, Nyquist PAM4 and Partial-Response PAM4 for Future 5G Ethernet-based Fronthaul Architecture](https://mlsanity.com/paper/1801.10574/) (2018)

## Abstract (arXiv)

With the growing demand for high-bandwidth applications like video streaming and cloud services, the data transfer rates required for wireline communication keeps increasing, making the channel loss a major obstacle in achieving low bit error rate (BER). Equalization techniques such as feed-forward equalizer (FFE) and decision feedback equalizer (DFE) are commonly used to compensate for channel loss in wireline communication, but they have limitations in terms of noise boosting and timing constraints. On the other hand, the forward-backward algorithm can achieve better BER performance, but its high complexity makes it impractical for wireline communication. In this work, we propose a novel neural network, NeuralEQ, that effectively mimics the forward-backward algorithm and performs better than FFE and DFE while reducing complexity of the forward-backward algorithm. Performance of NeuralEQ is verified through simulations using real channels.